humanitarian ict forum humanitarian dataworkﬂow … · erik / world food programme (wfp)...

MARCH 2017

HUMANITARIAN ICT FORUM

Humanitarian DataWorkflow Research

HUMANITARIAN ICT FORUM HUMANITARIAN DATA WORKFLOW RESEARCH

“How do we make data move faster?”

NAME SURNAME

BACKGROUND

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HUMANITARIAN ICT FORUM HUMANITARIAN DATA WORKFLOW RESEARCH 3

Early this year, the Paul Allen Foundation funded a collaborative research effort between UN OCHA, Roberta Tassi and frog.

BACKGROUND

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Research goals

BACKGROUND

1Map the humanitarian data workflow

2Identify factors that impact data flow

3Find ways for the HDX to help

5

DAKAR

CONAKRY

22 IN-FIELD

INTERVIEWS

14 SME

INTERVIEWS

14,884 MILES

TRAVELED

22 KOBO/HDX

INTERVIEWS


Key Insights


Framing the Problem

KEY INSIGHTS


High-Level Data Workflow

FRAMING THE PROBLEM


Defining Sharing

FRAMING THE PROBLEM

GENERATION

DATA EXPERTS

ENUMERATORS

IMO

CLUSTER / NGO HQGOVERNMENT

IMOs WORKING GROUP

RAW DATA DATA PRODUCT

PROJECTMANAGER

DATA TRANSFER& CLEANING

DATA ANALYSIS& VISUALIZATION

FIELDOFFICER

M&E /ANALYSTS

XLS PDF

ACCESS

MOBILE DATA COLLECTION INDICATORS AND RESOURCES

XLS


What impacts data flow?

KEY INSIGHTS

motivation“Data collection is human resource intensive.

How do we incentivize the process?”

JULIET / INTERNATIONAL ORGANIZATION FOR MIGRATION (IOM)


Missing and misaligned incentives are pervasive.

MOTIVATION

Data generation and access remain separate journeys in the workflow. Feedback loops are missing between HQ and the field.

OBSERVATIONS

There are few direct payoffs for field staff.

Field staff can’t see the value of their work.

Data collection is a secondary task.

OPPORTUNITIES

Make it easier for field teams to reap the benefits of data analysis.

Help data workers understand their role and contributions.

Provide better incentives to non-specialist data workers

“If you don’t create guidelines and consistency at the beginning, you need then an army of IMOs to correct errors.”

FREDERIC / AGENCY FOR TECHNICAL COOPERATION AND DEVELOPMENT (ACTED)

data literacy


Uneven knowledge among data workers drastically affects data flow.

DATA LITERACY

Experienced staff are asked to do too much; inexperienced staff add to their workload.

OBSERVATIONS

Data literacy at the field level is very low and best practices are delivered inconsistently.

IMOs become bottlenecks when they are asked to deal with basic data tasks.

Knowledge resides in people, not systems, and creates dangerous dependencies on local staff.

OPPORTUNITIES

Improve access to best practices and guide non-experts through basic data tasks.

Establish processes that redistribute basic data processing tasks away from IMOs to less expert workers.

Reduce reliance on individual staff through resilient systems and repeatable processes.

data processing“Not everyone knows how to use Excel look up,

or how to structure a database.”

ERIK / WORLD FOOD PROGRAMME (WFP)


Inadequate tools and supporting processes slow data processing.

DATA PROCESSING

Data processing and management tools don’t fit the skill level of the people who need to use them.

OBSERVATIONS

IMOs waste lots of time fixing avoidable errors introduced by poor tool use.

Cleaning and verifying data is a long and tedious process.

Baseline data are often unreliable and using data from multiple sources is challenging.

OPPORTUNITIES

Provide simpler tools with explicit guidance for low-expertise workers.

Automate simple data processing and collaboration tasks, like reviews and approvals.

Invest in high-value datasets like CODs and leverage HXL to improve data interoperability.

transparency“It’s important to give visibility to what organizations are doing.

Even if you can’t download the spreadsheet, you may decide to get in touch.”

JULIETTE / INTERNATIONAL ORGANIZATION FOR MIGRATION (IOM)


Coordination and collaboration challenges yield complex slowdowns.

TRANSPARENCY

Inefficiencies stem from a lack of visibility into collection efforts and baseline datasets used across a relief effort.

OBSERVATIONS

Difficulty seeing who is collecting what data, leads to overlaps and replications.

Even when data are discoverable, formatting differences can make them hard to use.

Mismatched or incompatible baseline data is common predicament.

OPPORTUNITIES

Empower planners by improving visibility around data collection activities at the outset of a crisis.

Leverage the Humanitarian Exchange Language (HXL) standard to ease collaboration within and between organizations.

Invest in and promote high-value baseline datasets. Good baseline data naturally encourages collaboration.

mindset“We need to keep control.”

YERO / MINISTRY OF HEALTH, CONAKRY


Cultural issues can stop data in its tracks.

MINDSET

Organizational politics coupled with and emotions like fear and possessiveness can have a strong dampening effect on sharing.

OBSERVATIONS

Fear of liability, damage, political consequences or loss of control—especially across org boundaries—all discourage public data sharing.

Data is thought of as intellectual property. People want recognition for creating it.

Getting permission and agreeing on public data sharing policies are significant extra work.

OPPORTUNITIES

Bypass public data fears by sharing metadata— methodology, indicators, ownership—before the data itself.

Ensure platforms support social signaling. Give data owners credit and make them look accomplished on HDX.

Establish reusable sharing policies where possible, and automate supporting data preparation tasks.


Five Factors Affect Flow

SUMMARY

Missing and misaligned incentives.

Uneven knowledge among data workers.

motivation data literacy data processing transparency mindsetInadequate tools and supporting processes.

Coordination and collaboration challenges.

Cultural and organizational issues.


Visit the Humanitarian Data Exchange at humdata.org

Learn about Humanitarian Exchange Language at hxlstandard.org

FIND OUT MORE

http://humdata.org

http://hxlstandard.org

humanitarian ict forum humanitarian dataworkﬂow … · erik / world food programme (wfp)...

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